System and method for using human driving patterns to manage speed control for autonomous vehicles

ABSTRACT

A system and method for using human driving patterns to manage speed control for autonomous vehicles are disclosed. A particular embodiment includes: generating data corresponding to desired human driving behaviors; training a human driving model module using a reinforcement learning process and the desired human driving behaviors; receiving a proposed vehicle speed control command; determining if the proposed vehicle speed control command conforms to the desired human driving behaviors by use of the human driving model module; and validating or modifying the proposed vehicle speed control command based on the determination.

PRIORITY/RELATED DOCUMENTS

This patent document claims the benefit of U.S. patent application Ser.No. 15/698,375, filed on Sep. 7, 2017, which is incorporated herein byreference in its entirety for all purposes.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the U.S. Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever. The following notice applies to the disclosure hereinand to the drawings that form a part of this document: Copyright2016-2020, TuSimple, All Rights Reserved.

TECHNICAL FIELD

This patent document pertains generally to tools (systems, apparatuses,methodologies, computer program products, etc.) for vehicle controlsystems, autonomous driving systems, vehicle control command generation,and more particularly, but not by way of limitation, to a system andmethod for using human driving patterns to manage speed control forautonomous vehicles.

BACKGROUND

An autonomous vehicle is often configured and controlled to follow atrajectory based on a computed driving path. However, when variablessuch as obstacles are present on the driving path, the autonomousvehicle must perform control operations so that the vehicle may besafely driven by changing the driving path or speed in real time. Theautonomous driving system or control system of the vehicle must makethese control adjustments to cause the vehicle to follow the desiredtrajectory and travel at the desired speed, while avoiding obstacles.However, these control adjustments can result in abrupt, uncomfortable,or even unsafe vehicle maneuvers.

SUMMARY

A system and method for using human driving patterns to manage speedcontrol for autonomous vehicles are disclosed herein. The variousexample embodiments described herein provide an autonomous drivingcontrol process that employs many parameters to control the speed of thevehicle. The example embodiments enable the autonomous vehicle toperform acceleration and deceleration modeled like a correspondingnatural human driving behavior by use of a reinforcement learningframework to learn how to tune these parameters in a mannercorresponding to the natural human driving behavior. The autonomousdriving control process starts with a coarsely trained parameter setthat allows the vehicle to function properly on the road or insimulation. While a vehicle is running on the road or in simulation, theautonomous driving control process collects the vehicle's speed profiledata over time and compares the achieved speed profile data withcorresponding human driving behavior data maintained in thereinforcement learning framework. Actual or simulated speed profilesthat closely follow the corresponding human behavior are rewarded, whileprofiles that deviate from corresponding human behavior are penalized.Based on these rewards/penalties, the autonomous driving control processcan tune its parameter set favoring profiles and behaviors that resemblethe corresponding human behavior.

BRIEF DESCRIPTION OF THE DRAWINGS

The various embodiments are illustrated by way of example, and not byway of limitation, in the figures of the accompanying drawings in which:

FIG. 1 illustrates a block diagram of an example ecosystem in which avehicle speed control module of an example embodiment can beimplemented;

FIG. 2 illustrates the components of the vehicle speed control system ofan example embodiment;

FIG. 3 illustrates an example embodiment including a process fortraining a human driving model module using a process calledreinforcement learning;

FIG. 4 illustrates an example embodiment wherein operational data for avehicle in operation can be captured at various points on a trajectoryalong which the vehicle is traveling;

FIG. 5 illustrates the reinforcement learning process in an exampleembodiment;

FIGS. 6 and 7 illustrate the operation of the reinforcement learningprocess in an example embodiment;

FIG. 8 is a process flow diagram illustrating an example embodiment of asystem and method for using human driving patterns to manage speedcontrol for autonomous vehicles; and

FIG. 9 shows a diagrammatic representation of machine in the exampleform of a computer system within which a set of instructions whenexecuted may cause the machine to perform any one or more of themethodologies discussed herein.

DETAILED DESCRIPTION

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the various embodiments. It will be evident, however,to one of ordinary skill in the art that the various embodiments may bepracticed without these specific details.

As described in various example embodiments, a system and method forusing human driving patterns to manage speed control for autonomousvehicles are described herein. An example embodiment disclosed hereincan be used in the context of an in-vehicle control system 150 in avehicle ecosystem 101. In one example embodiment, an in-vehicle controlsystem 150 with a vehicle speed control module 200 resident in a vehicle105 can be configured like the architecture and ecosystem 101illustrated in FIG. 1. However, it will be apparent to those of ordinaryskill in the art that the vehicle speed control module 200 described andclaimed herein can be implemented, configured, and used in a variety ofother applications and systems as well.

Referring now to FIG. 1, a block diagram illustrates an exampleecosystem 101 in which an in-vehicle control system 150 and a vehiclespeed control module 200 of an example embodiment can be implemented.These components are described in more detail below. Ecosystem 101includes a variety of systems and components that can generate and/ordeliver one or more sources of information/data and related services tothe in-vehicle control system 150 and the vehicle speed control module200, which can be installed in the vehicle 105. For example, a camerainstalled in the vehicle 105, as one of the devices of vehiclesubsystems 140, can generate image and timing data that can be receivedby the in-vehicle control system 150. The in-vehicle control system 150and an image processing module executing therein can receive this imageand timing data input. The image processing module can extract objectdata from the image and timing data to identify objects in the proximityof the vehicle 105. The in-vehicle control system 150 can process theobject data and generate a trajectory or motion control command for thevehicle 105 based on the detected objects. The trajectory or motioncontrol command can be used by an autonomous vehicle control subsystem,as another one of the subsystems of vehicle subsystems 140. In anexample embodiment, the in-vehicle control system 150 can generate avehicle control command signal, which can be used by a subsystem ofvehicle subsystems 140 to cause the vehicle to traverse the generatedtrajectory or move in a manner corresponding to the motion controlcommand. The autonomous vehicle control subsystem, for example, can usethe real-time generated trajectory and vehicle motion control commandsignal to safely and efficiently navigate the vehicle 105 through a realworld driving environment while avoiding obstacles and safelycontrolling the vehicle.

In an example embodiment as described herein, the in-vehicle controlsystem 150 can be in data communication with a plurality of vehiclesubsystems 140, all of which can be resident in a user's vehicle 105. Avehicle subsystem interface 141 is provided to facilitate datacommunication between the in-vehicle control system 150 and theplurality of vehicle subsystems 140. The in-vehicle control system 150can be configured to include a data processor 171 to execute the vehiclespeed control module 200 for processing data received from one or moreof the vehicle subsystems 140. The data processor 171 can be combinedwith a data storage device 172 as part of a computing system 170 in thein-vehicle control system 150. The data storage device 172 can be usedto store data, processing parameters, and data processing instructions.A processing module interface 165 can be provided to facilitate datacommunications between the data processor 171 and the vehicle speedcontrol module 200. In various example embodiments, a plurality ofprocessing modules, configured similarly to vehicle speed control module200, can be provided for execution by data processor 171. As shown bythe dashed lines in FIG. 1, the vehicle speed control module 200 can beintegrated into the in-vehicle control system 150, optionally downloadedto the in-vehicle control system 150, or deployed separately from thein-vehicle control system 150.

The in-vehicle control system 150 can be configured to receive ortransmit data from/to a wide-area network 120 and network resources 122connected thereto. An in-vehicle web-enabled device 130 and/or a usermobile device 132 can be used to communicate via network 120. Aweb-enabled device interface 131 can be used by the in-vehicle controlsystem 150 to facilitate data communication between the in-vehiclecontrol system 150 and the network 120 via the in-vehicle web-enableddevice 130. Similarly, a user mobile device interface 133 can be used bythe in-vehicle control system 150 to facilitate data communicationbetween the in-vehicle control system 150 and the network 120 via theuser mobile device 132. In this manner, the in-vehicle control system150 can obtain real-time access to network resources 122 via network120. The network resources 122 can be used to obtain processing modulesfor execution by data processor 171, data content to train internalneural networks, system parameters, or other data.

The ecosystem 101 can include a wide area data network 120. The network120 represents one or more conventional wide area data networks, such asthe Internet, a cellular telephone network, satellite network, pagernetwork, a wireless broadcast network, gaming network, WiFi network,peer-to-peer network, Voice over IP (VoIP) network, etc. One or more ofthese networks 120 can be used to connect a user or client system withnetwork resources 122, such as websites, servers, central control sites,or the like. The network resources 122 can generate and/or distributedata, which can be received in vehicle 105 via in-vehicle web-enableddevices 130 or user mobile devices 132. The network resources 122 canalso host network cloud services, which can support the functionalityused to compute or assist in processing object input or object inputanalysis. Antennas can serve to connect the in-vehicle control system150 and the vehicle speed control module 200 with the data network 120via cellular, satellite, radio, or other conventional signal receptionmechanisms. Such cellular data networks are currently available (e.g.,Verizon™, AT&T™, T-Mobile™, etc.). Such satellite-based data or contentnetworks are also currently available (e.g., SiriusXM™, HughesNet™,etc.). The conventional broadcast networks, such as AM/FM radionetworks, pager networks, UHF networks, gaming networks, WiFi networks,peer-to-peer networks, Voice over IP (VoIP) networks, and the like arealso well-known. Thus, as described in more detail below, the in-vehiclecontrol system 150 and the vehicle speed control module 200 can receiveweb-based data or content via an in-vehicle web-enabled device interface131, which can be used to connect with the in-vehicle web-enabled devicereceiver 130 and network 120. In this manner, the in-vehicle controlsystem 150 and the vehicle speed control module 200 can support avariety of network-connectable in-vehicle devices and systems fromwithin a vehicle 105.

As shown in FIG. 1, the in-vehicle control system 150 and the vehiclespeed control module 200 can also receive data, processing controlparameters, and training content from user mobile devices 132, which canbe located inside or proximately to the vehicle 105. The user mobiledevices 132 can represent standard mobile devices, such as cellularphones, smartphones, personal digital assistants (PDA's), MP3 players,tablet computing devices (e.g., iPad™), laptop computers, CD players,and other mobile devices, which can produce, receive, and/or deliverdata, processing control parameters, and content for the in-vehiclecontrol system 150 and the vehicle speed control module 200. As shown inFIG. 1, the mobile devices 132 can also be in data communication withthe network cloud 120. The mobile devices 132 can source data andcontent from internal memory components of the mobile devices 132themselves or from network resources 122 via network 120. Additionally,mobile devices 132 can themselves include a GPS data receiver,accelerometers, WiFi triangulation, or other geo-location sensors orcomponents in the mobile device, which can be used to determine thereal-time geo-location of the user (via the mobile device) at any momentin time. In any case, the in-vehicle control system 150 and the vehiclespeed control module 200 can receive data from the mobile devices 132 asshown in FIG. 1.

Referring still to FIG. 1, the example embodiment of ecosystem 101 caninclude vehicle operational subsystems 140. For embodiments that areimplemented in a vehicle 105, many standard vehicles include operationalsubsystems, such as electronic control units (ECUs), supportingmonitoring/control subsystems for the engine, brakes, transmission,electrical system, emissions system, interior environment, and the like.For example, data signals communicated from the vehicle operationalsubsystems 140 (e.g., ECUs of the vehicle 105) to the in-vehicle controlsystem 150 via vehicle subsystem interface 141 may include informationabout the state of one or more of the components or subsystems of thevehicle 105. In particular, the data signals, which can be communicatedfrom the vehicle operational subsystems 140 to a Controller Area Network(CAN) bus of the vehicle 105, can be received and processed by thein-vehicle control system 150 via vehicle subsystem interface 141.Embodiments of the systems and methods described herein can be used withsubstantially any mechanized system that uses a CAN bus or similar datacommunications bus as defined herein, including, but not limited to,industrial equipment, boats, trucks, machinery, or automobiles; thus,the term “vehicle” as used herein can include any such mechanizedsystems. Embodiments of the systems and methods described herein canalso be used with any systems employing some form of network datacommunications; however, such network communications are not required.

Referring still to FIG. 1, the example embodiment of ecosystem 101, andthe vehicle operational subsystems 140 therein, can include a variety ofvehicle subsystems in support of the operation of vehicle 105. Ingeneral, the vehicle 105 may take the form of a car, truck, motorcycle,bus, boat, airplane, helicopter, lawn mower, earth mover, snowmobile,aircraft, recreational vehicle, amusement park vehicle, farm equipment,construction equipment, tram, golf cart, train, and trolley, forexample. Other vehicles are possible as well. The vehicle 105 may beconfigured to operate fully or partially in an autonomous mode. Forexample, the vehicle 105 may control itself while in the autonomousmode, and may be operable to determine a current state of the vehicleand its environment, determine a predicted behavior of at least oneother vehicle in the environment, determine a confidence level that maycorrespond to a likelihood of the at least one other vehicle to performthe predicted behavior, and control the vehicle 105 based on thedetermined information. While in autonomous mode, the vehicle 105 may beconfigured to operate without human interaction.

The vehicle 105 may include various vehicle subsystems such as a vehicledrive subsystem 142, vehicle sensor subsystem 144, vehicle controlsubsystem 146, and occupant interface subsystem 148. As described above,the vehicle 105 may also include the in-vehicle control system 150, thecomputing system 170, and the vehicle speed control module 200. Thevehicle 105 may include more or fewer subsystems and each subsystemcould include multiple elements. Further, each of the subsystems andelements of vehicle 105 could be interconnected. Thus, one or more ofthe described functions of the vehicle 105 may be divided up intoadditional functional or physical components or combined into fewerfunctional or physical components. In some further examples, additionalfunctional and physical components may be added to the examplesillustrated by FIG. 1.

The vehicle drive subsystem 142 may include components operable toprovide powered motion for the vehicle 105. In an example embodiment,the vehicle drive subsystem 142 may include an engine or motor,wheels/tires, a transmission, an electrical subsystem, and a powersource. The engine or motor may be any combination of an internalcombustion engine, an electric motor, steam engine, fuel cell engine,propane engine, or other types of engines or motors. In some exampleembodiments, the engine may be configured to convert a power source intomechanical energy. In some example embodiments, the vehicle drivesubsystem 142 may include multiple types of engines or motors. Forinstance, a gas-electric hybrid car could include a gasoline engine andan electric motor. Other examples are possible.

The wheels of the vehicle 105 may be standard tires. The wheels of thevehicle 105 may be configured in various formats, including a unicycle,bicycle, tricycle, or a four-wheel format, such as on a car or a truck,for example. Other wheel geometries are possible, such as thoseincluding six or more wheels. Any combination of the wheels of vehicle105 may be operable to rotate differentially with respect to otherwheels. The wheels may represent at least one wheel that is fixedlyattached to the transmission and at least one tire coupled to a rim ofthe wheel that could make contact with the driving surface. The wheelsmay include a combination of metal and rubber, or another combination ofmaterials. The transmission may include elements that are operable totransmit mechanical power from the engine to the wheels. For thispurpose, the transmission could include a gearbox, a clutch, adifferential, and drive shafts. The transmission may include otherelements as well. The drive shafts may include one or more axles thatcould be coupled to one or more wheels. The electrical system mayinclude elements that are operable to transfer and control electricalsignals in the vehicle 105. These electrical signals can be used toactivate lights, servos, electrical motors, and other electricallydriven or controlled devices of the vehicle 105. The power source mayrepresent a source of energy that may, in full or in part, power theengine or motor. That is, the engine or motor could be configured toconvert the power source into mechanical energy. Examples of powersources include gasoline, diesel, other petroleum-based fuels, propane,other compressed gas-based fuels, ethanol, fuel cell, solar panels,batteries, and other sources of electrical power. The power source couldadditionally or alternatively include any combination of fuel tanks,batteries, capacitors, or flywheels. The power source may also provideenergy for other subsystems of the vehicle 105.

The vehicle sensor subsystem 144 may include a number of sensorsconfigured to sense information about an environment or condition of thevehicle 105. For example, the vehicle sensor subsystem 144 may includean inertial measurement unit (IMU), a Global Positioning System (GPS)transceiver, a RADAR unit, a laser range finder/LIDAR unit, and one ormore cameras or image capture devices. The vehicle sensor subsystem 144may also include sensors configured to monitor internal systems of thevehicle 105 (e.g., an O2 monitor, a fuel gauge, an engine oiltemperature). Other sensors are possible as well. One or more of thesensors included in the vehicle sensor subsystem 144 may be configuredto be actuated separately or collectively in order to modify a position,an orientation, or both, of the one or more sensors.

The IMU may include any combination of sensors (e.g., accelerometers andgyroscopes) configured to sense position and orientation changes of thevehicle 105 based on inertial acceleration. The GPS transceiver may beany sensor configured to estimate a geographic location of the vehicle105. For this purpose, the GPS transceiver may include areceiver/transmitter operable to provide information regarding theposition of the vehicle 105 with respect to the Earth. The RADAR unitmay represent a system that utilizes radio signals to sense objectswithin the local environment of the vehicle 105. In some embodiments, inaddition to sensing the objects, the RADAR unit may additionally beconfigured to sense the speed and the heading of the objects proximateto the vehicle 105. The laser range finder or LIDAR unit may be anysensor configured to sense objects in the environment in which thevehicle 105 is located using lasers. In an example embodiment, the laserrange finder/LIDAR unit may include one or more laser sources, a laserscanner, and one or more detectors, among other system components. Thelaser range finder/LIDAR unit could be configured to operate in acoherent (e.g., using heterodyne detection) or an incoherent detectionmode. The cameras may include one or more devices configured to capturea plurality of images of the environment of the vehicle 105. The camerasmay be still image cameras or motion video cameras.

The vehicle control system 146 may be configured to control operation ofthe vehicle 105 and its components. Accordingly, the vehicle controlsystem 146 may include various elements such as a steering unit, athrottle, a brake unit, a navigation unit, and an autonomous controlunit.

The steering unit may represent any combination of mechanisms that maybe operable to adjust the heading of vehicle 105. The throttle may beconfigured to control, for instance, the operating speed of the engineand, in turn, control the speed of the vehicle 105. The brake unit caninclude any combination of mechanisms configured to decelerate thevehicle 105. The brake unit can use friction to slow the wheels in astandard manner. In other embodiments, the brake unit may convert thekinetic energy of the wheels to electric current. The brake unit maytake other forms as well. The navigation unit may be any systemconfigured to determine a driving path or route for the vehicle 105. Thenavigation unit may additionally be configured to update the drivingpath dynamically while the vehicle 105 is in operation. In someembodiments, the navigation unit may be configured to incorporate datafrom the vehicle speed control module 200, the GPS transceiver, and oneor more predetermined maps so as to determine the driving path for thevehicle 105. The autonomous control unit may represent a control systemconfigured to identify, evaluate, and avoid or otherwise negotiatepotential obstacles in the environment of the vehicle 105. In general,the autonomous control unit may be configured to control the vehicle 105for operation without a driver or to provide driver assistance incontrolling the vehicle 105. In some embodiments, the autonomous controlunit may be configured to incorporate data from the vehicle speedcontrol module 200, the GPS transceiver, the RADAR, the LIDAR, thecameras, and other vehicle subsystems to determine the driving path ortrajectory for the vehicle 105. The vehicle control system 146 mayadditionally or alternatively include components other than those shownand described.

Occupant interface subsystems 148 may be configured to allow interactionbetween the vehicle 105 and external sensors, other vehicles, othercomputer systems, and/or an occupant or user of vehicle 105. Forexample, the occupant interface subsystems 148 may include standardvisual display devices (e.g., plasma displays, liquid crystal displays(LCDs), touchscreen displays, heads-up displays, or the like), speakersor other audio output devices, microphones or other audio input devices,navigation interfaces, and interfaces for controlling the internalenvironment (e.g., temperature, fan, etc.) of the vehicle 105.

In an example embodiment, the occupant interface subsystems 148 mayprovide, for instance, means for a user/occupant of the vehicle 105 tointeract with the other vehicle subsystems. The visual display devicesmay provide information to a user of the vehicle 105. The user interfacedevices can also be operable to accept input from the user via atouchscreen. The touchscreen may be configured to sense at least one ofa position and a movement of a user's finger via capacitive sensing,resistance sensing, or a surface acoustic wave process, among otherpossibilities. The touchscreen may be capable of sensing finger movementin a direction parallel or planar to the touchscreen surface, in adirection normal to the touchscreen surface, or both, and may also becapable of sensing a level of pressure applied to the touchscreensurface. The touchscreen may be formed of one or more translucent ortransparent insulating layers and one or more translucent or transparentconducting layers. The touchscreen may take other forms as well.

In other instances, the occupant interface subsystems 148 may providemeans for the vehicle 105 to communicate with devices within itsenvironment. The microphone may be configured to receive audio (e.g., avoice command or other audio input) from a user of the vehicle 105.Similarly, the speakers may be configured to output audio to a user ofthe vehicle 105. In one example embodiment, the occupant interfacesubsystems 148 may be configured to wirelessly communicate with one ormore devices directly or via a communication network. For example, awireless communication system could use 3G cellular communication, suchas CDMA, EVDO, GSM/GPRS, or 4G cellular communication, such as WiMAX orLTE. Alternatively, the wireless communication system may communicatewith a wireless local area network (WLAN), for example, using WIFI®. Insome embodiments, the wireless communication system 146 may communicatedirectly with a device, for example, using an infrared link, BLUETOOTH®,or ZIGBEE®. Other wireless protocols, such as various vehicularcommunication systems, are possible within the context of thedisclosure. For example, the wireless communication system may includeone or more dedicated short range communications (DSRC) devices that mayinclude public or private data communications between vehicles and/orroadside stations.

Many or all of the functions of the vehicle 105 can be controlled by thecomputing system 170. The computing system 170 may include at least onedata processor 171 (which can include at least one microprocessor) thatexecutes processing instructions stored in a non-transitory computerreadable medium, such as the data storage device 172. The computingsystem 170 may also represent a plurality of computing devices that mayserve to control individual components or subsystems of the vehicle 105in a distributed fashion. In some embodiments, the data storage device172 may contain processing instructions (e.g., program logic) executableby the data processor 171 to perform various functions of the vehicle105, including those described herein in connection with the drawings.The data storage device 172 may contain additional instructions as well,including instructions to transmit data to, receive data from, interactwith, or control one or more of the vehicle drive subsystem 140, thevehicle sensor subsystem 144, the vehicle control subsystem 146, and theoccupant interface subsystems 148.

In addition to the processing instructions, the data storage device 172may store data such as data processing parameters, training data, humandriving model data, human driving model parameters, roadway maps, andpath information, among other information. Such information may be usedby the vehicle 105 and the computing system 170 during the operation ofthe vehicle 105 in the autonomous, semi-autonomous, and/or manual modes.

The vehicle 105 may include a user interface for providing informationto or receiving input from a user or occupant of the vehicle 105. Theuser interface may control or enable control of the content and thelayout of interactive images that may be displayed on a display device.Further, the user interface may include one or more input/output deviceswithin the set of occupant interface subsystems 148, such as the displaydevice, the speakers, the microphones, or a wireless communicationsystem.

The computing system 170 may control the function of the vehicle 105based on inputs received from various vehicle subsystems (e.g., thevehicle drive subsystem 140, the vehicle sensor subsystem 144, and thevehicle control subsystem 146), as well as from the occupant interfacesubsystem 148. For example, the computing system 170 may use input fromthe vehicle control system 146 in order to control the steering unit toavoid an obstacle detected by the vehicle sensor subsystem 144 andfollow a path or trajectory generated by the vehicle speed controlmodule 200. In an example embodiment, the computing system 170 can beoperable to provide control over many aspects of the vehicle 105 and itssubsystems.

Although FIG. 1 shows various components of vehicle 105, e.g., vehiclesubsystems 140, computing system 170, data storage device 172, andvehicle speed control module 200, as being integrated into the vehicle105, one or more of these components could be mounted or associatedseparately from the vehicle 105. For example, data storage device 172could, in part or in full, exist separate from the vehicle 105. Thus,the vehicle 105 could be provided in the form of device elements thatmay be located separately or together. The device elements that make upvehicle 105 could be communicatively coupled together in a wired orwireless fashion.

Additionally, other data and/or content (denoted herein as ancillarydata) can be obtained from local and/or remote sources by the in-vehiclecontrol system 150 as described above. The ancillary data can be used toaugment, modify, or train the operation of the vehicle speed controlmodule 200 based on a variety of factors including, the context in whichthe user is operating the vehicle (e.g., the location of the vehicle,the specified destination, direction of travel, speed, the time of day,the status of the vehicle, etc.), and a variety of other data obtainablefrom the variety of sources, local and remote, as described herein.

In a particular embodiment, the in-vehicle control system 150 and thevehicle speed control module 200 can be implemented as in-vehiclecomponents of vehicle 105. In various example embodiments, thein-vehicle control system 150 and the vehicle speed control module 200in data communication therewith can be implemented as integratedcomponents or as separate components. In an example embodiment, thesoftware components of the in-vehicle control system 150 and/or thevehicle speed control module 200 can be dynamically upgraded, modified,and/or augmented by use of the data connection with the mobile devices132 and/or the network resources 122 via network 120. The in-vehiclecontrol system 150 can periodically query a mobile device 132 or anetwork resource 122 for updates or updates can be pushed to thein-vehicle control system 150.

Referring now to FIG. 2, a diagram illustrates the components of avehicle speed control system 201 with the vehicle speed control module200 of an example embodiment. In the example embodiment, the vehiclespeed control module 200 can be configured to include a speed controlmodule 173 and a human driving model module 175. As described in moredetail below, the speed control module 173 and the human driving modelmodule 175 serve to enable the modeling and modification of a vehiclespeed control command for the vehicle based on a comparison of aproposed vehicle speed control command 210 with corresponding normalhuman driving behavior data maintained by the human driving model module175. The speed control module 173 and the human driving model module 175can be configured as software modules executed by the data processor 171of the in-vehicle control system 150. The modules 173 and 175 of thevehicle speed control module 200 can receive a proposed vehicle speedcontrol command 210 and produce a validated or modified vehicle speedcontrol command 220, which can be used by the autonomous controlsubsystem of the vehicle control subsystem 146 to efficiently and safelycontrol the vehicle 105. As part of their vehicle speed control commandprocessing, the speed control module 173 and the human driving modelmodule 175 can be configured to work with human driving model parameters174, which can be used to configure and fine tune the operation of thevehicle speed control module 200. The human driving model parameters 174can be stored in a memory 172 of the in-vehicle control system 150.

In the example embodiment, the vehicle speed control module 200 can beconfigured to include an interface with the in-vehicle control system150, as shown in FIG. 1, through which the vehicle speed control module200 can send and receive data as described herein. Additionally, thevehicle speed control module 200 can be configured to include aninterface with the in-vehicle control system 150 and/or other ecosystem101 subsystems through which the vehicle speed control module 200 canreceive ancillary data from the various data sources described above. Asdescribed above, the vehicle speed control module 200 can also beimplemented in systems and platforms that are not deployed in a vehicleand not necessarily used in or with a vehicle.

In an example embodiment as shown in FIG. 2, the vehicle speed controlmodule 200 can be configured to include the speed control module 173 andthe human driving model module 175, as well as other processing modulesnot shown for clarity. Each of these modules can be implemented assoftware, firmware, or other logic components executing or activatedwithin an executable environment of the vehicle speed control module 200operating within or in data communication with the in-vehicle controlsystem 150. Each of these modules of an example embodiment is describedin more detail below in connection with the figures provided herein.

System and Method for Using Human Driving Patterns to Manage SpeedControl for Autonomous Vehicles

A system and method for using human driving patterns to manage speedcontrol for autonomous vehicles are disclosed herein. The variousexample embodiments described herein provide an autonomous drivingcontrol process that employs many parameters to control the speed of thevehicle. The example embodiments enable the autonomous vehicle toperform acceleration and deceleration modeled like a correspondingnatural human driving behavior by use of a reinforcement learningframework to learn how to tune these parameters in a mannercorresponding to the natural human driving behavior. The autonomousdriving control process starts with a coarsely trained parameter setthat allows the vehicle to function properly on the road or insimulation. While a vehicle is running on the road or in simulation, theautonomous driving control process collects the vehicle's speed profiledata over time and compares the achieved speed profile data withcorresponding human driving behavior data maintained in thereinforcement learning framework. Actual or simulated speed profilesthat closely follow the corresponding human behavior are rewarded, whileprofiles that deviate from corresponding human behavior are penalized.Based on these rewards/penalties, the autonomous driving control processcan tune its parameter set favoring profiles and behaviors that resemblethe corresponding human behavior.

An example embodiment can develop a human driving behavior model basedon data related to various types of driving behaviors captured andretained by the human driving model module 175 of the exampleembodiment. The example embodiment can use actual empirical datacaptured through vehicle sensor subsystems and driving simulation datato model typical human driving behaviors, particularly human drivingbehavior related to vehicle speed control. This empirical data andsimulation data is captured and used by the human driving model module175 to encode data corresponding to these typical driving behaviors asmathematical or data representations. The data can be encoded as aneural network, rules sets, or other well-known methods for developingmachine learning systems. The empirical data can be captured for asingle vehicle and/or aggregated from data collected from a largepopulation of vehicles and drivers. Over time, the human driving modelmodule 175 can learn typical driving behaviors, identify and retaindriving behaviors deemed normal and safe, and expunge behaviors deemedunsafe or residing outside common operational thresholds.

For example, an example embodiment can learn a common driving behaviorrelated to accelerating or decelerating an autonomous vehicle and/ormanaging the speed of the vehicle. The human driving model module 175can receive empirical data and simulation data related to drivingbehaviors that correspond to a throttle level or throttle percentageapplied to the engine or drivetrain controls of the vehicle as afunction of time. An initial increase in the throttle percentage for aperiod of time can indicate an accelerating or vehicle speed increasebehavior as typical when a vehicle passes an obstacle, such as anothervehicle on the roadway. The slope of the throttle percentage indicatesthe typical rate of acceleration for this type of driving behavior.Abrupt or unsafe acceleration rates, indicated by steep throttlepercentage slopes, can be detected and expunged from the human drivingmodel. In a corresponding fashion, a decelerating throttle percentagefor a period of time can indicate a decelerating action or a vehiclespeed decrease behavior. Abrupt or unsafe deceleration rates, indicatedby steep throttle percentage slopes, can be detected and expunged fromthe human driving model. Typically, when a vehicle is driven by humandrivers and the driver performs an acceleration or decelerationmaneuver, the relationship between the throttle percentage and time canbe learned and retained as a smooth data curve and a correspondingfunction by the human driving model module 175. As such, datacorresponding to these acceleration or deceleration behaviors can bereceived, retained as a mathematical or data representation, and learnedby the human driving model module 175 of an example embodiment.

An example embodiment can also learn a common driving behavior relatedto braking or stopping an autonomous vehicle and/or managing the speedof the vehicle. The human driving model module 175 can receive empiricaldata and simulation data related to driving behaviors that correspond toa braking level or braking percentage applied to the braking controls ofthe vehicle as a function of time. An initial increase in the brakingpercentage for a period of time can indicate a vehicle stopping behavioras typical when a driver depresses the brake pedal. The slope of thebraking percentage indicates the typical rate of braking for this typeof driving behavior. Abrupt or unsafe braking rates, indicated by steepbraking percentage slopes, can be detected and expunged from the humandriving model. In a corresponding fashion, a reduced or decreasingbraking percentage for a period of time can indicate a reduced vehiclebraking behavior. Typically, when a vehicle is driven by human driversand the driver performs a braking maneuver, the relationship between thebraking percentage and time can be learned and retained as a smooth datacurve and a corresponding function by the human driving model module175. As such, data corresponding to these braking behaviors can bereceived, retained as a mathematical or data representation, and learnedby the human driving model module 175 of an example embodiment.

An example embodiment can also learn a common human driving behavior,such as one related to steering an autonomous vehicle and/or passing anobstacle (e.g., another vehicle) in the roadway. The human driving modelmodule 175 can receive empirical data and simulation data related todriving behaviors that correspond to a steering angle applied to thesteering controls of the vehicle as a function of time. Abrupt,swerving, or unsafe turn rates, indicated by steep steering angleslopes, can be detected and expunged from the human driving model.Typically, when a vehicle is driven by human drivers and the driverperforms a left-side or right-side passing maneuver, the relationshipbetween the steering angle and time can be learned and retained as asmooth data curve and a corresponding function by the human drivingmodel module 175. As such, data corresponding to these steering andpassing behaviors can be received, retained as a mathematical or datarepresentation, and learned by the human driving model module 175 of anexample embodiment.

As described above, the human driving model module 175 of an exampleembodiment can develop a human driving behavior model based on datarelated to various types of driving behaviors. This data can beaggregated over many drivers, vehicles, driving scenarios, and drivingconditions. Sensor data and/or simulation data for the each of thevehicles being used to train the human driving behavior model can becaptured and plotted on a graph. The plot can represent human driverbehavior data based on sensor data received over time from sensorsand/or simulation data for each of a multitude of vehicles being used totrain the human driving behavior model. The data plotted on the graphcan also include data from a vehicle driving simulation system. Theplotted sensor or simulation data can provide a disbursed clusteringeffect produced by typical or normal driving behaviors that tend tofollow consistent trends or patterns. In other words, normal or typicaldrivers tend to exhibit similar driving behaviors. The plotted sensor orsimulation data can provide a relatively consistent disbursed clusteringeffect produced by the typical or normal driving behaviors that tend tofollow consistent trends or patterns. As such, the data clusters oftypical human driver behavior data can be identified and used to trainthe human driving model module 175 of an example embodiment using aprocess called reinforcement learning, which is described in more detailbelow.

Referring now to FIG. 3, an example embodiment includes a process fortraining the human driving model module 175 of an example embodimentusing a process called reinforcement learning. In an example embodiment,reinforcement learning uses driving simulation and actual on-the-roadtraining exercises to configure or tune parameters in (e.g., train) thehuman driving model module 175 to model desired human driving behaviors.In an initial operation 310, data corresponding to typical or normalhuman driving behaviors (e.g., vehicle operational data) can berecorded. The typical or normal human driving behaviors can include ahuman driver's management of speed, braking, and heading (e.g.,trajectory). As described above, this normal human driving behavior datacan be captured from sensors on an actual vehicle or obtained from adriving simulation system. The normal human driving behavior data can beretained in memory 172.

In operation 315 as shown in FIG. 3, the data generated by the humandriving model module 175 can be run against the normal human drivingbehavior data in a simulation training phase. As part of a reinforcementlearning process of the simulation training phase (operation 320), theincremental speeds (or other vehicle operational data) of a vehicle insimulation at particular time steps are compared with the incrementalspeeds that would be generated by the human driving model module 175 atcorresponding time steps. The differences of the speeds (or othervehicle operational data) at the corresponding time steps between thesimulated vehicle and the human driving model module 175 are calculatedand compared to an acceptable error or variance threshold. If thedifference between the speed at a corresponding time step between thesimulated vehicle and the human driving model module 175 is above orgreater than the acceptable error or variance threshold, correspondingparameters within the human driving model module 175 are updated(operation 317) to reduce the error or variance and cause the differencebetween the speed at the corresponding time step between the simulatedvehicle and the human driving model module 175 to be below, less than,or equal to the acceptable error or variance threshold. Control can thenbe passed to operation 315 where another iteration of the simulationtraining is performed.

Referring still to FIG. 3, the simulation training phase and thereinforcement learning process as described above can be performed foras many iterations as needed to cause the update of parameters in thehuman driving model module 175 to progress toward a diminishingdifferential or deviation relative to the acceptable error or variancethreshold. Eventually, the behaviors produced by the human driving modelmodule 175 will converge on conformity with the corresponding behaviorsproduced in the vehicle simulation system against which the humandriving model module 175 is trained in the simulation training phase.

To further refine the human driving behavior modeled by the humandriving model module 175, an example embodiment can also run the dataproduced by the human driving model module 175 against the normal humandriving behavior data in an actual on-the-road training phase. In theactual on-the-road training phase, actual human driving behavior datacan be captured from a vehicle operating in a real world trafficenvironment (operation 325). For example, as shown in FIG. 4,operational data for a vehicle in operation can be captured at variouspoints on a trajectory along which the vehicle is traveling. Thecaptured data can include the vehicle's position, velocity,acceleration, incremental speed, target speed, and a variety of otherdata corresponding to the vehicle's status at a particular point intime. As part of the reinforcement learning process in the actualon-the-road training phase (FIG. 3, operation 330), the incrementalspeeds (or other vehicle operational data) of the actual vehicle atparticular time steps are compared with the incremental speeds (or othervehicle operational data) that would be generated by the human drivingmodel module 175 at corresponding time steps. The differences of thespeeds (or other vehicle operational data) at the corresponding timesteps between the actual vehicle and the human driving model module 175are calculated and compared to the acceptable error or variancethreshold. If the difference between the speed at a corresponding timestep between the actual vehicle and the human driving model module 175is above or greater than the acceptable error or variance threshold,corresponding parameters within the human driving model module 175 areupdated (operation 327) to reduce the error or variance and cause thedifference between the speed at the corresponding time step between theactual vehicle and the human driving model module 175 to be below, lessthan, or equal to the acceptable error or variance threshold. Controlcan then be passed to operation 325 where another iteration of theactual on-the-road training is performed.

Referring still to FIG. 3, the actual on-the-road training phase and thereinforcement learning process as described above can be performed foras many iterations as needed to cause the update of parameters in thehuman driving model module 175 to progress toward a diminishingdifferential or deviation relative to the acceptable error or variancethreshold. Eventually, the behaviors produced by the human driving modelmodule 175 will converge on conformity with the corresponding behaviorsproduced in the actual vehicle with a human driver against which thehuman driving model module 175 is trained in the actual on-the-roadtraining phase. Given the training of the human driving model module 175using the simulation phase and the actual on-the-road training phase asdescribed above, the parameters in the human driving model module 175will be finely tuned to produce human driving behaviors that closelycorrelate with the corresponding simulated and actual human drivingbehaviors.

Referring now to FIG. 5, a diagram illustrates the reinforcementlearning process in an example embodiment. As shown, the state of avehicle in simulation or actual operation can be determined by capturingvehicle operational data (e.g., the vehicle's position, velocity,acceleration, incremental speed, target speed, and a variety of othervehicle operational data) at particular points in time. As describedabove, this vehicle operational data can be compared with theincremental speeds (or other vehicle operational data) that would begenerated by the human driving model module 175 at corresponding timesteps. The differences of the speeds (or other vehicle operational data)at the corresponding time steps between the actual vehicle and the humandriving model module 175 are calculated and compared to the acceptableerror or variance threshold. These differences represent the deviationof the behavior of the vehicle in its current state from thecorresponding desired human driving behavior. This deviation can bedenoted the reward or penalty corresponding to the current behavior ofthe vehicle. If the difference between the speed at a corresponding timestep between the actual vehicle and the human driving model module 175is above or greater than the acceptable error or variance threshold(e.g., a penalty condition), corresponding parameters within the humandriving model module 175 are updated or trained to reduce the error orvariance and cause the difference between the speed at the correspondingtime step between the actual vehicle and the human driving model module175 to be below, less than, or equal to the acceptable error or variancethreshold (e.g., a reward condition). The updated vehicle controlparameters can be provided as an output to the human driving modelmodule 175 and another iteration of the simulation or actual on-the-roadtraining can be performed.

Referring to FIGS. 6 and 7, the diagrams illustrate the operation of thereinforcement learning process in an example embodiment. As shown inFIG. 6, a modeled vehicle is initially on a trajectory learned from afirst iteration of the reinforcement learning process as describedabove. In this first iteration, the deviation of the trajectory learnedfrom a first iteration of the reinforcement learning process as comparedto the desired human driving trajectory (shown by example in FIG. 6) isdetermined and used to update the parameters in the human driving modelmodule 175 as described above. A next (second) iteration of thereinforcement learning process is performed. As shown in FIG. 6, thetrajectory learned from the second iteration of the reinforcementlearning process produces a deviation from the desired human drivingtrajectory that is less than the deviation from the first iteration. Assuch, the trajectory of the modeled vehicle is beginning to convergewith the trajectory of the desired human driving behavior. As subsequentiterations of the reinforcement learning process are performed, thedeviation from the desired human driving trajectory continues todecrease and the trajectory of the modeled vehicle continues to convergewith the trajectory of the desired human driving behavior. As shown inFIG. 7, the deviation of the behavior of the modeled vehicle relative tothe desired human driving behavior decreases rapidly over the timedevoted to training the human driving model module 175 using thereinforcement learning process as described herein.

Referring again to FIG. 2, an example embodiment includes a process forcomparing a proposed vehicle speed control command 210 with the normalhuman driver speed control behaviors learned by the human driving modelmodule 175 as described above to determine if the proposed vehicle speedcontrol command 210 corresponds with normal human driver speed controlbehavior. If the proposed vehicle speed control command 210 conforms tothe normal human driver speed control behavior, the proposed vehiclespeed control command 210 is validated by the speed control module 173and passed through as a validated vehicle speed control command 220. Ifthe proposed vehicle speed control command 210 does not conform to thenormal human driver speed control behavior, the proposed vehicle speedcontrol command 210 is modified by the speed control module 173 toconform to the normal human driver speed control behavior and passedthrough as a modified vehicle speed control command 220. During theprocess of controlling the movement of the vehicle 105, the in-vehiclecontrol subsystem 150 can issue many control commands to the vehiclecontrol subsystems 146 to perform a variety of driving behaviors ormaneuvers. Prior to actually commanding the vehicle control subsystems146 to perform a particular maneuver, the in-vehicle control subsystem150 can issue a proposed vehicle speed control command 210 to thevehicle speed control module 200. As described in detail above, thevehicle speed control module 200, and the speed control module 173therein, can determine if the proposed vehicle speed control command 210conforms to the normal human driver speed control behavior and canvalidate or modify the vehicle speed control command 210 accordingly. Inother words, the speed control module 173 can cause the proposed vehiclespeed control command 210 to model a typical or normal human driverspeed control behavior.

Referring now to FIG. 8, a flow diagram illustrates an exampleembodiment of a system and method 1000 for using human driving patternsto manage speed control. The example embodiment can be configured for:generating data corresponding to desired human driving behaviors(processing block 1010); training a human driving model module using areinforcement learning process and the desired human driving behaviors(processing block 1020); receiving a proposed vehicle speed controlcommand (processing block 1030); determining if the proposed vehiclespeed control command conforms to the desired human driving behaviors byuse of the human driving model module (processing block 1040); andvalidating or modifying the proposed vehicle speed control command basedon the determination (processing block 1050).

As used herein and unless specified otherwise, the term “mobile device”includes any computing or communications device that can communicatewith the in-vehicle control system 150 and/or the vehicle speed controlmodule 200 described herein to obtain read or write access to datasignals, messages, or content communicated via any mode of datacommunications. In many cases, the mobile device 130 is a handheld,portable device, such as a smart phone, mobile phone, cellulartelephone, tablet computer, laptop computer, display pager, radiofrequency (RF) device, infrared (IR) device, global positioning device(GPS), Personal Digital Assistants (PDA), handheld computers, wearablecomputer, portable game console, other mobile communication and/orcomputing device, or an integrated device combining one or more of thepreceding devices, and the like. Additionally, the mobile device 130 canbe a computing device, personal computer (PC), multiprocessor system,microprocessor-based or programmable consumer electronic device, networkPC, diagnostics equipment, a system operated by a vehicle 119manufacturer or service technician, and the like, and is not limited toportable devices. The mobile device 130 can receive and process data inany of a variety of data formats. The data format may include or beconfigured to operate with any programming format, protocol, or languageincluding, but not limited to, JavaScript, C++, iOS, Android, etc.

As used herein and unless specified otherwise, the term “networkresource” includes any device, system, or service that can communicatewith the in-vehicle control system 150 and/or the vehicle speed controlmodule 200 described herein to obtain read or write access to datasignals, messages, or content communicated via any mode of inter-processor networked data communications. In many cases, the network resource122 is a data network accessible computing platform, including client orserver computers, websites, mobile devices, peer-to-peer (P2P) networknodes, and the like. Additionally, the network resource 122 can be a webappliance, a network router, switch, bridge, gateway, diagnosticsequipment, a system operated by a vehicle 119 manufacturer or servicetechnician, or any machine capable of executing a set of instructions(sequential or otherwise) that specify actions to be taken by thatmachine. Further, while only a single machine is illustrated, the term“machine” can also be taken to include any collection of machines thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methodologies discussed herein. Thenetwork resources 122 may include any of a variety of providers orprocessors of network transportable digital content. Typically, the fileformat that is employed is Extensible Markup Language (XML), however,the various embodiments are not so limited, and other file formats maybe used. For example, data formats other than Hypertext Markup Language(HTML)/XML or formats other than open/standard data formats can besupported by various embodiments. Any electronic file format, such asPortable Document Format (PDF), audio (e.g., Motion Picture ExpertsGroup Audio Layer 3—MP3, and the like), video (e.g., MP4, and the like),and any proprietary interchange format defined by specific content sitescan be supported by the various embodiments described herein.

The wide area data network 120 (also denoted the network cloud) usedwith the network resources 122 can be configured to couple one computingor communication device with another computing or communication device.The network may be enabled to employ any form of computer readable dataor media for communicating information from one electronic device toanother. The network 120 can include the Internet in addition to otherwide area networks (WANs), cellular telephone networks, metro-areanetworks, local area networks (LANs), other packet-switched networks,circuit-switched networks, direct data connections, such as through auniversal serial bus (USB) or Ethernet port, other forms ofcomputer-readable media, or any combination thereof. The network 120 caninclude the Internet in addition to other wide area networks (WANs),cellular telephone networks, satellite networks, over-the-air broadcastnetworks, AM/FM radio networks, pager networks, UHF networks, otherbroadcast networks, gaming networks, WiFi networks, peer-to-peernetworks, Voice Over IP (VoIP) networks, metro-area networks, local areanetworks (LANs), other packet-switched networks, circuit-switchednetworks, direct data connections, such as through a universal serialbus (USB) or Ethernet port, other forms of computer-readable media, orany combination thereof. On an interconnected set of networks, includingthose based on differing architectures and protocols, a router orgateway can act as a link between networks, enabling messages to be sentbetween computing devices on different networks. Also, communicationlinks within networks can typically include twisted wire pair cabling,USB, Firewire, Ethernet, or coaxial cable, while communication linksbetween networks may utilize analog or digital telephone lines, full orfractional dedicated digital lines including T1, T2, T3, and T4,Integrated Services Digital Networks (ISDNs), Digital User Lines (DSLs),wireless links including satellite links, cellular telephone links, orother communication links known to those of ordinary skill in the art.Furthermore, remote computers and other related electronic devices canbe remotely connected to the network via a modem and temporary telephonelink.

The network 120 may further include any of a variety of wirelesssub-networks that may further overlay stand-alone ad-hoc networks, andthe like, to provide an infrastructure-oriented connection. Suchsub-networks may include mesh networks, Wireless LAN (WLAN) networks,cellular networks, and the like. The network may also include anautonomous system of terminals, gateways, routers, and the likeconnected by wireless radio links or wireless transceivers. Theseconnectors may be configured to move freely and randomly and organizethemselves arbitrarily, such that the topology of the network may changerapidly. The network 120 may further employ one or more of a pluralityof standard wireless and/or cellular protocols or access technologiesincluding those set forth herein in connection with network interface712 and network 714 described in the figures herewith.

In a particular embodiment, a mobile device 132 and/or a networkresource 122 may act as a client device enabling a user to access anduse the in-vehicle control system 150 and/or the vehicle speed controlmodule 200 to interact with one or more components of a vehiclesubsystem. These client devices 132 or 122 may include virtually anycomputing device that is configured to send and receive information overa network, such as network 120 as described herein. Such client devicesmay include mobile devices, such as cellular telephones, smart phones,tablet computers, display pagers, radio frequency (RF) devices, infrared(IR) devices, global positioning devices (GPS), Personal DigitalAssistants (PDAs), handheld computers, wearable computers, gameconsoles, integrated devices combining one or more of the precedingdevices, and the like. The client devices may also include othercomputing devices, such as personal computers (PCs), multiprocessorsystems, microprocessor-based or programmable consumer electronics,network PC's, and the like. As such, client devices may range widely interms of capabilities and features. For example, a client deviceconfigured as a cell phone may have a numeric keypad and a few lines ofmonochrome LCD display on which only text may be displayed. In anotherexample, a web-enabled client device may have a touch sensitive screen,a stylus, and a color LCD display screen in which both text and graphicsmay be displayed. Moreover, the web-enabled client device may include abrowser application enabled to receive and to send wireless applicationprotocol messages (WAP), and/or wired application messages, and thelike. In one embodiment, the browser application is enabled to employHyperText Markup Language (HTML), Dynamic HTML, Handheld Device MarkupLanguage (HDML), Wireless Markup Language (WML), WMLScript, JavaScript™,EXtensible HTML (xHTML), Compact HTML (CHTML), and the like, to displayand send a message with relevant information.

The client devices may also include at least one client application thatis configured to receive content or messages from another computingdevice via a network transmission. The client application may include acapability to provide and receive textual content, graphical content,video content, audio content, alerts, messages, notifications, and thelike. Moreover, the client devices may be further configured tocommunicate and/or receive a message, such as through a Short MessageService (SMS), direct messaging (e.g., Twitter), email, MultimediaMessage Service (MMS), instant messaging (IM), internet relay chat(IRC), mIRC, Jabber, Enhanced Messaging Service (EMS), text messaging,Smart Messaging, Over the Air (OTA) messaging, or the like, betweenanother computing device, and the like. The client devices may alsoinclude a wireless application device on which a client application isconfigured to enable a user of the device to send and receiveinformation to/from network resources wirelessly via the network.

The in-vehicle control system 150 and/or the vehicle speed controlmodule 200 can be implemented using systems that enhance the security ofthe execution environment, thereby improving security and reducing thepossibility that the in-vehicle control system 150 and/or the vehiclespeed control module 200 and the related services could be compromisedby viruses or malware. For example, the in-vehicle control system 150and/or the vehicle speed control module 200 can be implemented using aTrusted Execution Environment, which can ensure that sensitive data isstored, processed, and communicated in a secure way.

FIG. 9 shows a diagrammatic representation of a machine in the exampleform of a computing system 700 within which a set of instructions whenexecuted and/or processing logic when activated may cause the machine toperform any one or more of the methodologies described and/or claimedherein. In alternative embodiments, the machine operates as a standalonedevice or may be connected (e.g., networked) to other machines. In anetworked deployment, the machine may operate in the capacity of aserver or a client machine in server-client network environment, or as apeer machine in a peer-to-peer (or distributed) network environment. Themachine may be a personal computer (PC), a laptop computer, a tabletcomputing system, a Personal Digital Assistant (PDA), a cellulartelephone, a smartphone, a web appliance, a set-top box (STB), a networkrouter, switch or bridge, or any machine capable of executing a set ofinstructions (sequential or otherwise) or activating processing logicthat specify actions to be taken by that machine. Further, while only asingle machine is illustrated, the term “machine” can also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions or processing logic to performany one or more of the methodologies described and/or claimed herein.

The example computing system 700 can include a data processor 702 (e.g.,a System-on-a-Chip (SoC), general processing core, graphics core, andoptionally other processing logic) and a memory 704, which cancommunicate with each other via a bus or other data transfer system 706.The mobile computing and/or communication system 700 may further includevarious input/output (I/O) devices and/or interfaces 710, such as atouchscreen display, an audio jack, a voice interface, and optionally anetwork interface 712. In an example embodiment, the network interface712 can include one or more radio transceivers configured forcompatibility with any one or more standard wireless and/or cellularprotocols or access technologies (e.g., 2nd (2G), 2.5, 3rd (3G), 4th(4G) generation, and future generation radio access for cellularsystems, Global System for Mobile communication (GSM), General PacketRadio Services (GPRS), Enhanced Data GSM Environment (EDGE), WidebandCode Division Multiple Access (WCDMA), LTE, CDMA2000, WLAN, WirelessRouter (WR) mesh, and the like). Network interface 712 may also beconfigured for use with various other wired and/or wirelesscommunication protocols, including TCP/IP, UDP, SIP, SMS, RTP, WAP,CDMA, TDMA, UMTS, UWB, WiFi, WiMax, Bluetooth©, IEEE 802.11x, and thelike. In essence, network interface 712 may include or support virtuallyany wired and/or wireless communication and data processing mechanismsby which information/data may travel between a computing system 700 andanother computing or communication system via network 714.

The memory 704 can represent a machine-readable medium on which isstored one or more sets of instructions, software, firmware, or otherprocessing logic (e.g., logic 708) embodying any one or more of themethodologies or functions described and/or claimed herein. The logic708, or a portion thereof, may also reside, completely or at leastpartially within the processor 702 during execution thereof by themobile computing and/or communication system 700. As such, the memory704 and the processor 702 may also constitute machine-readable media.The logic 708, or a portion thereof, may also be configured asprocessing logic or logic, at least a portion of which is partiallyimplemented in hardware. The logic 708, or a portion thereof, mayfurther be transmitted or received over a network 714 via the networkinterface 712. While the machine-readable medium of an exampleembodiment can be a single medium, the term “machine-readable medium”should be taken to include a single non-transitory medium or multiplenon-transitory media (e.g., a centralized or distributed database,and/or associated caches and computing systems) that store the one ormore sets of instructions. The term “machine-readable medium” can alsobe taken to include any non-transitory medium that is capable ofstoring, encoding or carrying a set of instructions for execution by themachine and that cause the machine to perform any one or more of themethodologies of the various embodiments, or that is capable of storing,encoding or carrying data structures utilized by or associated with sucha set of instructions. The term “machine-readable medium” canaccordingly be taken to include, but not be limited to, solid-statememories, optical media, and magnetic media.

The Abstract of the Disclosure is provided to allow the reader toquickly ascertain the nature of the technical disclosure. It issubmitted with the understanding that it will not be used to interpretor limit the scope or meaning of the claims. In addition, in theforegoing Detailed Description, it can be seen that various features aregrouped together in a single embodiment for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter lies in less than allfeatures of a single disclosed embodiment. Thus, the following claimsare hereby incorporated into the Detailed Description, with each claimstanding on its own as a separate embodiment.

What is claimed is:
 1. A system comprising: a data processor; and avehicle speed control module, executable by the data processor,configured to: receive a proposed vehicle speed control command prior tocommanding a vehicle control subsystem of an autonomous vehicle toperform a maneuver corresponding to the proposed vehicle speed controlcommand; determine if the proposed vehicle speed control commandconforms to standards of human driving behaviors by use of a humandriving model module, executable by the data processor; validate ormodify the proposed vehicle speed control command based on thedetermination; and cause the autonomous vehicle to perform a maneuvercorresponding to the validated or modified vehicle speed controlcommand.
 2. The system of claim 1 wherein the vehicle speed controlmodule is further configured to train the human driving model moduleusing a reinforcement learning process comprising a simulation training,wherein data generated by the human driving model module is run againstdata corresponding to the human driving behaviors during the simulationtraining phase.
 3. The system of claim 1 wherein the vehicle speedcontrol module is further configured to train the human driving modelmodule using a reinforcement learning process comprising an actualon-the-road training phase, wherein data generated by the human drivingmodel module is run against data corresponding to the human drivingbehaviors captured by sensors of the autonomous vehicle.
 4. The systemof claim 1 wherein the vehicle speed control module is furtherconfigured to train the human driving model module by modifyingparameters in the human driving model module based on a reinforcementlearning process, wherein the parameters are used for validating ormodifying the proposed vehicle speed control command.
 5. The system ofclaim 4 wherein the parameters are corresponding to at least one of aspeed, a braking, and a heading of the autonomous vehicle.
 6. The systemof claim 1 wherein the human driving model module is trained with datacorresponding to the human driving behaviors.
 7. The system of claim 1wherein the vehicle speed control module is further configured to trainthe human driving model module by determining a current state of theautonomous vehicle and determining a deviation between the current stateof the autonomous vehicle and a state corresponding to the human drivingbehaviors, wherein parameters in the human driving model module aremodified based on the deviation between the current state of theautonomous vehicle and the state corresponding to the human drivingbehaviors.
 8. The system of claim 7 wherein a trained human drivingmodel module is having modified parameters, wherein the deviationdetermined by the human driving model module is larger than thedeviation determined by the trained human driving model module.
 9. Amethod comprising: receiving a proposed vehicle speed control commandprior to commanding a vehicle control subsystem of an autonomous vehicleto perform a maneuver corresponding to the proposed vehicle speedcontrol command; determining if the proposed vehicle speed controlcommand conforms to standards of human driving behaviors by use of ahuman driving model module, executable by a data processor; validatingor modifying the proposed vehicle speed control command based on thedetermination; and cause the autonomous vehicle to perform a maneuvercorresponding to the validated or modified vehicle speed controlcommand.
 10. The method of claim 9 further comprising training the humandriving model module with data corresponding to the human drivingbehaviors, wherein the data corresponding to the human driving behaviorsis encoded as a neural network or a rules set.
 11. The method of claim 9further comprising outputting the validated or modified vehicle speedcontrol command to the vehicle control subsystem, causing the autonomousvehicle to perform the maneuver, wherein the maneuver comprisesfollowing a trajectory.
 12. The method of claim 9 further comprisingcapturing data through vehicle sensor subsystems and driving simulationdata to model the human driving behaviors.
 13. The method of claim 9further comprising training the human driving model module with datacorresponding to the human driving behaviors, wherein the datacorresponding to the human driving behaviors is captures by sensors ofthe autonomous vehicle, wherein the data corresponding to the humandriving behaviors comprises at least a position, an acceleration, anincremental speed, and a target speed of the autonomous vehicle.
 14. Themethod of claim 9 comprising training the human driving model modulebased on a reinforcement learning process comprising an actualon-the-road training phase, with data corresponding to the human drivingbehaviors, wherein the data corresponding to the human driving behaviorscomprises incremental speeds of the autonomous vehicle at time steps.15. The method of claim 14 wherein data of the incremental speeds of theautonomous vehicle at the time steps, generated by the human drivingmodel module, is a first data, wherein data of the incremental speeds ofthe autonomous vehicle at the time steps, captured by sensors of theautonomous vehicle, is a second data, wherein the first data is comparedwith the second data during the actual on-the-road training phase. 16.The method of claim 15 wherein a difference between the first data andthe second data is calculated and compared to a threshold.
 17. Themethod of claim 16 wherein parameters of the human driving model moduleare used for validating or modifying the proposed vehicle speed controlcommand, wherein if the difference between the first data and the seconddata is greater than the threshold, the corresponding parameters aremodified.
 18. A non-transitory machine-useable storage medium embodyinginstructions which, when executed by a machine, cause the machine to:receive a proposed vehicle speed control command prior to commanding avehicle control subsystem of an autonomous vehicle to perform a maneuvercorresponding to the proposed vehicle speed control command; determineif the proposed vehicle speed control command conforms to standards ofhuman driving behaviors by use of a human driving model module,executable by the machine; validate or modify the proposed vehicle speedcontrol command based on the determination; and cause the autonomousvehicle to perform a maneuver corresponding to the validated or modifiedvehicle speed control command.
 19. The non-transitory machine-useablestorage medium of claim 18 being further configured to train the humandriving model module by modifying parameters in the human driving modelmodule based on a reinforcement learning process comprising an actualon-the-road training phase, wherein data generated by the human drivingmodel module is a first data, wherein data captured by sensors of theautonomous vehicle is a second data, wherein a difference between thefirst data and the second data is calculated and compared to athreshold.
 20. The non-transitory machine-useable storage medium ofclaim 19 wherein the parameters are used for validating or modifying theproposed vehicle speed control command, wherein if the differencebetween the first data and the second data is greater than thethreshold, the corresponding parameters are modified.